Proportional and Reachable Cluster Teleoperation of a Distributed Multi-Robot System
Bibliographic record
Abstract
A remote team of robots may be teleoperated by multiple users to explore unstructured environments and to tackle unforeseen emergencies therein. During a large-scale environmental search, each user may visually observe a unique hazard endangering the remote robot connected to their local robot. Therefore, each user may want to tele-drive the remote robot team to a location different than the target locations of other users. This paper resolves the possible conflicts among the multiple user commands through a distributed clustering algorithm that allocates to each user a number of remote robots proportional to the urgency of their request. A pivotal design challenge in the teleoperation context is to ensure that the remote robots allocated to each user are topologically reachable from the user’s local robot within the induced communication subnetwork. The proposed design overcomes this challenge through a reachability-constrained integer linear program that modulates the interconnections of the remote robots on the fly. A comparative experiment on a platform with 2 local and 12 remote robots validates the practical efficacy of the proposed clustering algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".